Learning and updating internal visual models

学习和更新内部视觉模型

• 批准号: 8990935
• 负责人: ADAM KOHN
• 金额: $ 57.53万
• 依托单位: ALBERT EINSTEIN COLLEGE OF MEDICINE, INC
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-30 至 2018-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8990935
• 关键词: Affect; Animals; Area; Autistic Disorder; Behavior; Biological; Brain; Code; Computer Simulation; Data; Detection; Development; Disease; Electroencephalography; Elements; Environment; Event; Fatigue; Goals; Health; Human; Individual; Investigation; Knowledge; Learning; Machine Learning; Mission; Modeling; Monkeys; Neurons; Pattern; Perception; Process; Property; Prosthesis; Public Health; Recording of previous events; Research; Research Personnel; Scheme; Schizophrenia; Sensory; Sensory Process; Signal Transduction; Stimulus; Strategic Planning; Structure; Testing; Time; Uncertainty; Update; Visual; Visual Cortex; Visual Perception; Work; area V4; area striata; awake; base; brain machine interface; cognitive neuroscience; computational neuroscience; design; discount; expectation; extrastriate visual cortex; human subject; improved; meetings; neuromechanism; neurophysiology; novel; phenomenological models; prevent; public health relevance; relating to nervous system; response; statistics; treatment strategy; visual adaptation; visual process; visual processing

 DESCRIPTION (provided by applicant): In line with the strategic plan of the NEI, this project is focused on filling a profound gap in our understanding of neural mechanisms of visual perception. Specifically, we aim to understand how the adaptation of visual cortical circuits contributes to perception. Adaptation is a ubiquitous process by which neural processing and perception are dramatically influenced by recent visual inputs. However, the functional purpose of adaptation is poorly understood. Based on preliminary data, this project tests the hypothesis that visual adaptation instantiates a form of predictive coding, which is used to make unexpected events salient. We posit that cortical circuits learn the statistical structure of visua input in a manner that extends beyond previous fatigue- based descriptions of adaptation effects. This learning is used to discount expected features and signal novel ones. Our project will test this hypothesis through the collaborative effort of three investigators with expertise in human EEG, animal neurophysiology, and computational modeling. Aim 1 will assess the ability of cortical circuits to adapt to temporal sequences of input and to signal deviations from expected sequences. Aim 2 will evaluate the effect of stimulus uncertainty on adaptation and responses to novel events. Aim 3 will determine how adaptation dynamics and responses to novel stimuli are influenced by the temporal constancy of stimulus statistics. Each of these aims involves an experimental manipulation that yields distinct behavior from fatigue- based and predictive coding mechanisms. Thus, together our aims will provide a robust test of our core hypothesis, and provide a much richer understanding of the adaptive properties of cortical circuits. Results from our project will contribute to answering one of the continuing puzzles in visual research, which is to understand the functional purpose of adaptive mechanisms in visual perception.

 描述（由适用提供）：根据NEI的战略计划，该项目的重点是填补我们对视觉感知神经元机制的深刻差距。具体而言，我们旨在了解视觉皮层电路的适应如何有助于感知。适应是一个无处不在的过程，通过该过程，神经元处理和感知受到最近的视觉输入的影响。但是，适应的功能目的是很差的。基于初步数据，该项目检验了以下假设：视觉适应实例化了一种预测编码的形式，该编码用于使意外事件显着。我们肯定的是，皮质回路以一种超出了以前基于疲劳的适应效应的描述的方式来学习Visua输入的统计结构。该学习用于打折预期功能和信号新颖的功能。我们的项目将通过三位具有专业知识的调查员的合作努力来检验这一假设 人EEG，动物神经生理学和计算建模。 AIM 1将评估皮质回路适应输入临时序列并信号与预期序列的偏差的能力。 AIM 2将评估刺激不确定性对适应和对新事件的反应的影响。 AIM 3将确定适应动态和对新型刺激统计的反应。这些目的中的每一个都涉及实验性操作，该操作与基于疲劳和预测性编码机制产生不同的行为。这就是我们的目标将提供对我们的核心假设的强大检验，并对皮质回路的适应性特性提供了更丰富的理解。我们项目的结果将有助于回答视觉研究中持续的难题之一，即了解自适应机制在视觉感知中的功能目的。